Biology subjects

Preissl, H.

Publications and source records attributed to Preissl, H..

10 recordsLinked to original sources

bioRxiv2025

Frequency tagging evidence supports perceptual separation of rapid stimuli in human fetuses

In early human development, perceptual processes grow faster with maturation, as inferred using the duration of the attentional blink and multisensory integration window. The consequences of this developmental trend for sensory-cognition in fetuses are unclear: does the fetus perceive rapid stimuli as discrete events or, rather, one fused stimulus? We addressed this question using frequency tagging in two experiments with rapid auditory stimuli while neural responses were recorded in the third trimester with fetal magnetoencephalography (MEG). Our results are the first successful demonstration of frequency tagging in the fetal MEG amplitude spectrum and show that the fetal cortex generates separate neural responses to discrete auditory stimuli in both experiments, and similar results were also obtained when one experiment was repeated in newborns. While we cannot rule out perceptual fusion of rapid stimuli in higher-order association cortices, our results weaken the hypothesis that fetuses fuse rapid auditory stimuli into a single prolonged percept. Finally, our work points to frequency tagging analysis as a solution which avoids the uncertainties surrounding immature neural response latencies in time-domain analysis of fetal MEG.

neuroscience↗

bioRxiv2024

Predictive learning shapes the representational geometry of the human brain

Predictive coding theories propose that the brain constantly updates its internal models of the world to minimize prediction errors and optimize sensory processing. However, the neural mechanisms that link the encoding of prediction errors and optimization of sensory representations remain unclear. Here, we provide direct evidence how predictive learning shapes the representational geometry of the human brain. We recorded magnetoencephalography (MEG) in human participants listening to acoustic sequences with different levels of regularity. Representational similarity analysis revealed how, through learning, the brain aligned its representational geometry to match the statistical structure of the sensory inputs, by clustering the representations of temporally contiguous and predictable stimuli. Crucially, we found that in sensory areas the magnitude of the representational shift correlated with the encoding strength of prediction errors. Furthermore, using partial information decomposition we found that, prediction errors were processed by a synergistic network of high-level associative and sensory areas. Importantly, the strength of synergistic encoding of precition errors predicted the magnitude of representational alignment during learning. Our findings provide evidence that large-scale neural interactions engaged in predictive processing modulate the representational content of sensory areas, which may enhance the efficiency of perceptual processing in response to the statistical regularities of the environment.

neuroscience↗

bioRxiv2024

Influence of insulin sensitivity on food cue evoked functional brain connectivity in children

ObjectiveInsulin resistance during childhood is a risk factor for developing type 2 diabetes and other health problems later in life. Studies in adults have shown that insulin resistance affects regional and network activity in the brain which are vital for behavior, e.g. ingestion and metabolic control. To date, no study has investigated whether brain responses to food cues in children are associated with peripheral insulin sensitivity. MethodsWe included 53 children (36 girls) between the age of 7-11 years, who underwent an oral Glucose Tolerance Test (oGTT) to estimate peripheral insulin sensitivity (ISI). Brain responses were measured using functional magnetic resonance imaging (fMRI) before and after glucose ingestion. We compared food-cue task-based activity and functional connectivity (FC) between children with low and high ISI, adjusted for age and BMIz. ResultsIndependent of prandial state (i.e., glucose ingestion), children with lower ISI showed higher FC between the anterior insula and caudate and lower FC between the posterior insula and mid temporal cortex than children with higher ISI. Sex differences were found based on prandial state and peripheral insulin sensitivity in the insular FC. No differences were found on whole-brain food-cue reactivity. ConclusionsChildren with low peripheral insulin sensitivity showed differences in food cue evoked response particularly in insula functional connectivity. These differences might influence eating behavior and future risk of developing diabetes.

neuroscience↗

bioRxiv2023

Multiscale entropy analysis of combined EEG-fNIRS measurement in preterm neonates

In nature, biological systems such as the human brain are characterized by complex and non-linear dynamics. One way of quantifying signal complexity is Multiscale Entropy (MSE), which is suitable for structures with long-range correlation at different time scales. In developmental neuroscience, MSE can be taken as an index of brain maturation, and can differentiate between healthy and pathological development. In our current work, we explored the developmental trends of MSE on the basis of 30 simultaneous EEG - fNIRS recordings in premature infants between 27 and 34 weeks of gestational age (wGA). To explore potential factors impacting MSE, we determined the relation between MSE and the EEG Power Spectrum Density (PSD) and Spontaneous Activity Transients (SATs). As a result, via wGA, the MSE calculated on the EEG increases, thus reflecting the maturational processes in the brain networks, whereas in the fNIRS, MSE decreases, which might indicate a maturation of the brain blood supply. Moreover, we propose that the EEG power in the beta band (13-30 Hz) might be the main contributor to MSE in the EEG. Finally, we highlight the importance of SATs in determining MSE as calculated from the fNIRS recordings. HighlightsBiological systems show complex and non-linear dynamics. With Multiscale Entropy (MSE) we studied simultaneous EEG-fNIRS in premature infants. MSE in the EEG increases over gestational age, MSE in the fNIRS decreases. EEG power spectrum density and spontaneous activity transients contribute to MSE.

neuroscience↗

bioRxiv2022

The complexity of event-related MEG signals decreases with maturation in human fetuses and newborns

The complexity of neural activity is a commonly used read-out of healthy functioning in cortical circuits. Prior work has linked neural complexity to the level of maternal care in preterm infants at risk for developing mental disorders, yet the evolution of neural complexity in early human development is largely unknown. We hypothesized that cortical dynamics would evolve to optimize information processing as birth approaches, thereby increasing the complexity of cortical activity. To test this hypothesis, we conducted the first ever study relating prenatal neural complexity to maturation. MEG recordings were obtained from a sample of fetuses and newborns, including longitudinal data before and after birth. Using cortical responses to auditory irregularities, we computed several entropy measures which reflect the complexity of the MEG signal. Despite our hypothesis, neural complexity significantly decreased with maturation in both fetuses and newborns. Furthermore, we found that complexity decreased significantly faster in male fetuses for most entropy measures. Our surprising results lay the groundwork for the first ever mapping of how neural complexity evolves in early human development, with important implications for future efforts to develop predictive biomarkers of psychiatric disorders based on the complexity of perinatal MEG signals.

neuroscience↗

bioRxiv2020

Magnetoencephalographic Signatures of Conscious Processing Before Birth

The concept of fetal consciousness is a widely discussed topic. In this study, we applied a hierarchical rule learning paradigm to investigate the possibility of fetal conscious processing during the last trimester of pregnancy. We used fetal magnetoencephalography, to assess fetal brain activity in 56 healthy fetuses between gestational week 25 and 40, during an auditory oddball paradigm containing first- and second-order regularities. The comparison of fetal brain responses towards standard and deviant tones revealed that the investigated fetuses show signs of hierarchical rule learning, and thus the formation of a memory trace for the second-order regularity. This ability develops over the course of the last trimester of gestation, in accordance with processes in physiological brain development and was only reliably present in fetuses older than week 35 of gestation. Analysis of fetal autonomic nervous system activity replicates findings in newborns, showing importance of activity state for cognitive processes. On the whole, our results support the assumption that fetuses in the last weeks of gestation are capable of consciously processing stimuli that reach them from outside the womb.

neuroscience↗

bioRxiv2020

Dynamics of nonlinguistic statistical learning: From neural entrainment to the emergence of explicit knowledge

Humans are highly attuned to patterns in the environment. This ability to detect environmental patterns, referred to as statistical learning, plays a key role in many diverse aspects of cognition. However, the spatiotemporal neural mechanisms underlying implicit statistical learning, and how these mechanisms may relate or give rise to explicit learning, remain poorly understood. In the present study, we investigated these different aspects of statistical learning by using an auditory nonlinguistic statistical learning paradigm combined with magnetoencephalography. Twenty-four healthy volunteers were exposed to structured and random tone sequences, and statistical learning was quantified by neural entrainment. Already early during exposure, participants showed strong entrainment to the embedded tone patterns. A significant increase in entrainment over exposure was detected only in the structured condition, reflecting the trajectory of learning. While source reconstruction revealed a wide range of brain areas involved in this process, entrainment in areas around the left pre-central gyrus as well as right temporo-frontal areas significantly predicted behavioral performance. Sensor level results confirmed this relationship between neural entrainment and subsequent explicit knowledge. These results give insights into the dynamic relation between neural entrainment and explicit learning of triplet structures, suggesting that these two aspects are systematically related yet dissociable. Neural entrainment reflects robust, implicit learning of underlying patterns, whereas the emergence of explicit knowledge, likely built on the implicit encoding of structure, varies across individuals and may depend on factors such as sufficient exposure time and attention.

neuroscience↗

bioRxiv2020

Magnetoencephalographic Signatures of Hierarchical Rule Learning in Newborns

Estimating the extent to which newborn humans process input from their environment, especially regarding the depth of processing, is a challenging question. To approach this problem, we measured brain responses in 20 newborns with magnetoencephalography (MEG) in a "local-global" auditory oddball paradigm in which two-levels of hierarchical regularities are presented. Results suggest that infants in the first weeks of life are able to learn hierarchical rules, yet a certain level of vigilance seems to be necessary. Newborns detected violations of the first-order regularity and displayed a mismatch response between 200-400ms. Violations of the second-order regularity only evoked a late response in newborns in an active state, which was expressed by a high heart rate variability. These findings are in line with those obtained in human adults and older infants suggesting a continuity in the functional architecture from term- birth on, despite the important immaturity of the human brain at this age.

neuroscience↗

bioRxiv2019

A pilot Study: Auditory Steady-State Responses (ASSR) can be measured in human Fetuses using fetal Magnetoencephalography (fMEG)

BackgroundAuditory steady-state responses (ASSRs) are ongoing evoked brain responses to continuous auditory stimuli that play a role for auditory processing of complex sounds and speech perception. Transient auditory event-related responses (AERRs) have previously been recorded using fetal magnetoencephalography (fMEG) but involve different neurological pathways. Previous studies in children and adults demonstrated that the cortical components of the ASSR are significantly affected by state of consciousness and by maturational changes in neonates and young infants. To our knowledge, this is the first study to investigate ASSRs in human fetuses.\n\nMethods47 fMEG sessions were conducted with 24 healthy pregnant women in three gestational age groups (30-32 weeks, 33-35 weeks and 36-39 weeks). The stimulation consisted of amplitude-modulated (AM) tones with a duration of one second, a carrier frequency (CF) of 500 Hz and a modulation frequency (MF) of 27 Hz or 42 Hz. Both tones were presented in a random order with equal probability adding up to 80-100 repetitions per tone. The ASSR across trials was quantified by assessing phase synchrony in the cortical signals at the stimulation frequency.\n\nResults and ConclusionTen out of 47 recordings were excluded due to technical problems or maternal movements. Analysis of the included 37 fetal recordings revealed a statistically significant response for the phase coherence between trials for the MF of 27 Hz but not for 42 Hz. An exploratory subgroup analysis moreover suggested an advantage in detectability for fetal behavioral state 2F (active asleep) compared to 1F (quiet asleep) detected using fetal heart rate. In conclusion, with the present study it was possible to detect human fetal ASSRs for the first time. These findings warrant further investigations of the developing fetal brain.

neuroscience↗

bioRxiv2019

Evaluating Complexity of Fetal MEG Signals: A Comparison of Different Metrics and Their Applicability

In this work, we aim to investigate whether information based metrics of neural activity are a useful tool for the search for consciousness before and shortly after birth. Neural activity is measured using fetal magnetoencephalography (fMEG) in human fetuses and neonates. Based on recent theories on consciousness, information-based metrics are established to measure brain complexity and to assess different levels of consciousness. Different metrics (measures of entropy, compressibility and fractality) are, thus, explored in a reference population and their usability is evaluated. For comparative analysis, two fMEG channels were selected: one where brain activity was previously detected and one at least 15cm away, that represented a control channel. The usability of each metric was evaluated and results from the brain and control channel were compared. Concerning the ease of use with fMEG data, Lempel-Ziv-Welch (LZW) compression was evaluated as best, as it is unequivocal and needs low computational effort. The fractality measures have a high parameter space and therefore forfeit comparability, while entropy measures require a higher computational effort and more parameters to adjust compared to LZW. Comparison of a channel with brain activity and a control channel in neonatal recordings showed significant differences in most complexity metrics. This clear difference can be seen as proof of concept for the usability of complexity metrics in fMEG. For fetal data, this comparison produced less clear results which can be related to leftover maternal signals included in the control channel. Further work is necessary to conclusively interpret results from the analysis of fetal recordings. Yet this study shows that complexity metrics can be used for fMEG data on early consciousness and the evaluation gives a guidance for future work. The inconsistency of results from different metrics highlights the challenges of working with complexity metrics as neural correlates of consciousness, as well as the caution one should apply to interpret them.

neuroscience↗